Traffic Congestion Prediction using Deep Convolutional Neural Networks: A Color-coding Approach

Adnan, Mirza Fuad; Ahmed, Nadim; Ishraque, Imrez; Amin, Md. Sifath Al; Hasan, Md. Sumit

doi:10.1109/iceet56468.2022.10007425

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…This section first describes the basic architecture of the ConVNNs-LSTM followed by their combination with the LSTM for forecasting short-term traffic. The ConVNNs are effective in combining the spatio-temporal features in predicting traffic behavior rather than considering a single feature [52]. They are also efficient for large size networks.…”

Section: Traffic Prediction Using Convolutional Neural Network Lstm (...mentioning

confidence: 99%

Short-Term Traffic Prediction Using Deep Learning Long Short-Term Memory: Taxonomy, Applications, Challenges, and Future Trends

Khan,

Fouda,

et al. 2023

IEEE Access

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This paper surveys the short-term road traffic forecast algorithms based on the long-short term memory (LSTM) model of deep learning. The algorithms developed in the last three years are studied and analyzed. This provides an in-depth and thorough description of the algorithms rather than their marginal description as performed in the existing surveys that focus on general deep learning algorithms. The chosen algorithms are classified depending upon the use of LSTM in combination with other techniques for processing input data features towards a final traffic forecast. The operational strategies of the algorithms are described with merits and limitations. Moreover, a comparative analysis of the compared classes of algorithms is also provided. These strategies are helpful in selection of the right algorithms and their classes for the diverse traffic conditions and their future investigation for improvement. Besides, the applications of these classes of algorithms to traffic forecast in various networks for the latest decade is graphically depicted. Moreover, the applications of the LSTM in other fields involving a forecast are provided. Finally, the challenges associated with the short-term traffic forecast using the LSTM are described and strategies are highlighted for their future investigation.INDEX TERMS short-term traffic prediction, long short-term memory, LSTM, deep learning, intelligent transportation.

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Section: Traffic Prediction Using Convolutional Neural Network Lstm (...mentioning

confidence: 99%

Short-Term Traffic Prediction Using Deep Learning Long Short-Term Memory: Taxonomy, Applications, Challenges, and Future Trends

Khan,

Fouda,

et al. 2023

IEEE Access

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Efficient Road Traffic Video Congestion Classification Based on the Multi-Head Self-Attention Vision Transformer Model

Khalladi,

Ouessai,

Benamara

et al. 2024

Transport and Telecommunication Journal

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Due to rapid population growth, traffic congestion has become one of the major issues in urban areas. The utilization of technology may help to address this issue. This paper proposes a new Multi-head Self-attention Vision Transformer (MSViT) based macroscopic approach, for road traffic congestion classification. To evaluate this approach, we use the UCSD (University of California San Diego) dataset that includes different weather conditions (clear, overcast and rainy) and different traffic scenarios (light, medium and heavy). The classification accuracy reached a high level of 99.76% with this dataset and 99.37% when night-mode frames are added to it. The proposed MSViT based method outperforms the state-of-the-art macroscopic and microscopic methods that have been evaluated using the same UCSD dataset, which makes it an efficient solution for traffic congestion prediction.

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Traffic Congestion Prediction using Deep Convolutional Neural Networks: A Color-coding Approach

Cited by 2 publications

References 16 publications

Short-Term Traffic Prediction Using Deep Learning Long Short-Term Memory: Taxonomy, Applications, Challenges, and Future Trends

Short-Term Traffic Prediction Using Deep Learning Long Short-Term Memory: Taxonomy, Applications, Challenges, and Future Trends

Efficient Road Traffic Video Congestion Classification Based on the Multi-Head Self-Attention Vision Transformer Model

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